Computational Neural Networks Driving Complex Analytical Problem Solving

Recently, I attended a seminar at which a historian of women presented a dazzling interpretation of the polemical writing of Olympe de Gouges and its (not to mention her) reception during the French Revolution. A crusty old historian of the Revolution rose during the question period and inquired, in his own eastern twang, “Now that I know that women were participants in the Revolution, what difference does it make!” This encounter suggested to me what I will argue are two increasingly urgent tasks for women’s history: producing analytical problem-solving studies as well as descriptive and interpretive ones, and connecting their findings to general questions already on the historical agenda. This is not a call for integrating women’s history into other history, since that process may mean simply adding material on women and gender without analyzing its implications, but for writing analytical women’s history and connecting its problems to those of other histories. Only through such an endeavor is women’s history likely to change the agenda of history as a whole.

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Computational neural networks for the evaluation of biosensor FIA measurements

Analytica Chimica Acta ◽

10.1016/s0003-2670(97)00153-0 ◽

1997 ◽

Vol 348 (1-3) ◽

pp. 135-141 ◽

Cited By ~ 16

Author(s):

B. Hitzmann ◽

A. Ritzka ◽

R. Ulber ◽

T. Scheper ◽

K. Schügerl

Keyword(s):

Neural Networks ◽

Computational Neural Networks

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Teaching Analytical Instrumental Analysis to Undergraduates in Specialized Degree Programs with Heterogeneous Prior Knowledge: Reflections on Inquiry Based Lecture Activities

Journal of Scholarly Engagement ◽

10.9743/jeo.2018.1.2.6 ◽

2018 ◽

Vol 1 (2) ◽

Author(s):

Kirsten Tucker ◽

Peter Pingerelli

Keyword(s):

Problem Solving ◽

Prior Knowledge ◽

Instrumental Analysis ◽

Forensic Sciences ◽

Analytical Problem ◽

Knowledge Gaps ◽

Problem Solving Skills ◽

Instructional Assistant ◽

Scientific Narratives ◽

Modern Instrumentation

An adjunct faculty member and graduate instructional assistant (GIA) introduced inquiry-based activities into a 20-student undergraduate analytical instrumental analysis (AIA) lecture course, and reflect on their teaching assumptions, practices and experiences. The increased need for interdisciplinary scientific programs now has an AIA course serving multiple Bachelor of Science degrees in environmental sciences, forensic sciences, molecular biology, and secondary science education. However, we learned degree specialization also introduces into a course, student populations possessing heterogeneous prior knowledge, making an instructor’s rendering of student prerequisite skills a greater challenge. Guided by a pretest assessment, instructional activities were modified or developed by the authors and aimed at enhancing student engagement and motivation to mitigate prior knowledge gaps, improve analytical problem-solving skills, and facilitate a deeper understanding of modern instrumentation design and function. Detailed activity rationale and descriptions are presented. Activities included using readily available Internet bioinformatics and database tools for analytical problem-solving; demonstrating principles of electronic hardware and software design and integration; and creating interdisciplinary scientific narratives using biological, environmental, and industrial molecular exemplars. Our teaching reflections reference weekly post-lecture instructor/GIA discussions, strategic student questioning, collaborative classroom activity observations, and formative assessments. We propose continual instructional reflection is essential for a course serving multiple specialized degrees programs in a scientific field and facilitates preparation for students entering the workforce or graduate school. Further, our observations suggest inquiry-based, real-world activities relevant to modern instrumentation and its applications, assisted students in resolving heterogeneous prior knowledge gaps.

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